Scheduling for Ground-Assisted Federated Learning in LEO Satellite Constellations
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Abstract (arXiv)
Distributed training of machine learning models directly on satellites in low Earth orbit (LEO) is considered. Based on a federated learning (FL) algorithm specifically targeted at the unique challenges of the satellite scenario, we design a scheduler that exploits the predictability of visiting times between ground stations (GS) and satellites to reduce model staleness. Numerical experiments show that this can improve the convergence speed by a factor three.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Jun 2022.
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Citing papers per year: 2023: 5; 2024: 3; 2025: 1.
9 papers.
- 2023FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networkscited by 38
- 2023Optimizing Federated Learning in LEO Satellite Constellations via Intra-Plane Model Propagation and Sink Satellite Schedulingcited by 10
- 2024Satellite Federated Edge Learning: Architecture Design and Convergence Analysiscited by 10
- 2023One-Shot Federated Learning for LEO Constellations that Reduces Convergence Time from Days to 90 Minutescited by 7
- 2023Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloadingcited by 5
- 2023Secure Vertical Federated Learning Under Unreliable Connectivitycited by 4
- 2024Energy-Aware Federated Learning in Satellite Constellationscited by 3
- 2024Scheduling for On-Board Federated Learning with Satellite Clusterscited by 2
- 2025Communication-Efficient Learning for Satellite Constellationscited by 0
- 2025Communication-Efficient Learning for Satellite Constellations25 Nov 2025
- 2024Energy-Aware Federated Learning in Satellite Constellations23 Sep 2024
- 2024Satellite Federated Edge Learning: Architecture Design and Convergence Analysis2 Apr 2024
- 2024Scheduling for On-Board Federated Learning with Satellite Clusters14 Feb 2024
- 2023Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloading23 Dec 2023
- 2023FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks2 Nov 2023
- 2023Secure Vertical Federated Learning Under Unreliable Connectivity26 May 2023
- 2023One-Shot Federated Learning for LEO Constellations that Reduces Convergence Time from Days to 90 Minutes21 May 2023
- 2023Optimizing Federated Learning in LEO Satellite Constellations via Intra-Plane Model Propagation and Sink Satellite Scheduling27 Feb 2023